Multi-Location Microphone Noise Testing in Autonomous Vehicles
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Solution Overview
Problem
Conventional noise testing in autonomous vehicles is limited to a single location, typically the driver's seat, and does not account for variations in noise levels at other passenger locations, nor does it consider non-engine sources like passenger conversations or voice-based user interfaces.
Innovation Solution
A method involving microphones positioned at multiple locations in the passenger compartment to collect sound data under different operating conditions, allowing for the determination of noise levels associated with the vehicle, which can help in selecting quieter components and improving passenger experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional noise testing is limited to a single location (driver's seat), then testing complexity is reduced, but measurement precision is insufficient to capture noise variations across different passenger locations
Solution Approach 1:
The patent divides the passenger compartment into multiple testing locations (e.g., driver's seat, front passenger seat, rear seats) and places microphones at each location. This segmentation allows noise levels to be measured independently at each position, capturing spatial variations in noise while maintaining manageable system complexity through modular microphone placement and data collection.
2Measurement precision
If only engine and operational noises are tested, then testing time is reduced, but measurement precision fails to capture all noise sources including passenger conversations and voice-based user interface sounds
Solution Approach 1:
The patent implements preliminary classification of noise sources into categories (engine noise, operational noise, passenger conversation, voice-based user interface). By pre-defining these categories and associated measurement protocols, the system can efficiently target specific noise sources without requiring exhaustive testing of all possible sounds, thus improving detection precision while controlling testing duration.
3Reliability
If noise testing does not account for different operating conditions, then testing complexity is reduced, but the reliability of noise level data decreases for selecting quieter components
Solution Approach 1:
The patent implements dynamic testing protocols that automatically adjust measurement parameters based on operating conditions. The system detects current operating conditions (e.g., engine running, vehicle speed, ambient temperature) and dynamically modifies microphone sensitivity, sampling rates, and analysis methods accordingly. This dynamic adaptation ensures reliable noise data across diverse operating scenarios while managing complexity through automated condition-based adjustments.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables comprehensive noise level assessment across the vehicle, allowing for improved design and passenger experience by identifying and mitigating noise sources, and establishing design criteria for components to maintain acceptable noise levels.
Implementation Method 1
collecting first data indicative of a first sound level by one or more microphones
Data Source
AI summary
The present disclosure provides systems and methods for determining a noise level associated with an autonomous vehicle. The autonomous vehicle can include a passenger compartment configured to house one or more passengers. A method can include positioning one or more microphones at one or more locations in the passenger compartment, collecting first data indicative of a first sound level by the one or more microphones with the autonomous vehicle operating at a first operating condition, implementing an operating condition change for the autonomous vehicle from the first operating condition to a second operating condition, collecting second data indicative of a second sound level by the one or more microphones, and determining a noise level associated with the autonomous vehicle based at least in part on the first data and the second data.